How to Reduce Kubernetes Costs: 10 Proven Strategies

Kubernetes makes it easy to deploy applications but hard to control costs. Most clusters waste 30-50% of their budget on over-provisioned resources.

Here are 10 proven strategies to reduce your Kubernetes costs, ranked by impact.

1. Right-Size Resource Requests (30-50% savings)

This is the #1 cost reduction lever. Most pods request 3-5x more CPU and memory than they actually use.

How to fix it:

1. Analyze actual usage over 7-14 days
2. Set requests to P95 of actual usage
3. Set limits to 1.5-2x requests

curl -sL wozz.io/audit.sh | bash

2. Use Spot/Preemptible Instances (60-80% savings)

Spot instances cost 60-80% less than on-demand. Use them for:

• Stateless workloads
• Batch jobs
• Dev/staging environments
• Any workload that can handle restarts

Implementation: Use node selectors or taints to route appropriate workloads to spot node pools.

3. Delete Orphaned Resources (Immediate savings)

Orphaned resources are pure waste—they bill you for nothing. Check for:

• Load balancers without backend pods (~$20/month each)
• Persistent volumes not bound to any PVC
• Snapshots from deleted volumes
• Unused node pools

# Find orphaned LoadBalancers
kubectl get svc -A | grep LoadBalancer

# Find unbound PVs
kubectl get pv | grep Available

4. Implement Cluster Autoscaler (10-30% savings)

The cluster autoscaler adds nodes when needed and removes them when idle. Without it, you're paying for peak capacity 24/7.

Key settings:

• scale-down-unneeded-time: 5m – How long before scaling down
• scale-down-utilization-threshold: 0.5 – Scale down if utilization below 50%

5. Use Horizontal Pod Autoscaler (10-20% savings)

HPA scales pods based on CPU, memory, or custom metrics. This ensures you only run what you need.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

6. Schedule Non-Critical Workloads Off-Peak (20-40% savings)

Batch jobs, backups, and reports don't need to run during business hours. Schedule them for nights/weekends when you can use smaller node pools.

7. Use Namespace Resource Quotas

Without quotas, any team can deploy unlimited resources. Set limits per namespace:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: team-quota
  namespace: team-payments
spec:
  hard:
    requests.cpu: "10"
    requests.memory: 20Gi
    limits.cpu: "20"
    limits.memory: 40Gi

8. Optimize Container Images (5-10% savings)

Smaller images = faster pulls = less egress costs = faster scaling.

• Use multi-stage builds
• Start from alpine or distroless base images
• Remove dev dependencies from production images

9. Use Reserved Instances for Baseline (30-40% savings)

For predictable, always-on workloads, reserved instances (1-year or 3-year) save 30-60% vs on-demand.

Strategy: Use reserved for your baseline, on-demand for variable load, and spot for spikes.

10. Monitor and Alert on Cost Anomalies

Set up alerts for:

• Daily spend exceeding threshold
• New namespaces consuming resources
• Pods scaling beyond expected limits

Quick Wins vs Long-Term Strategies

Summary

Reducing Kubernetes costs doesn't require complex tools or major architecture changes. Start with visibility (audit your current waste), implement quick wins (delete orphaned resources, right-size top offenders), then build sustainable practices (autoscaling, spot instances, monitoring).

Most teams can cut 30-50% of their Kubernetes bill within 30 days using these strategies.